scholarly journals Modified Genetic Algorithm to Traveling Salesman Problem for Large Input Datasets

2016 ◽  
Vol 3 (1) ◽  
Author(s):  
Vladimir Vladimirov* ◽  
Fatima Sapundzhi ◽  
Radoslava Kraleva ◽  
Velin Kralev
Keyword(s):  
Genetic Algorithm ◽  
Complete Graph ◽  
Input Data ◽  
Travelling Salesman Problem ◽  
Optimal Solution ◽  
Solution Space ◽  
Hamiltonian Cycles ◽  
Large Size ◽  
Parameter Values

The use of graphs is widely applied in modeling and solving problems in the field of computer science and bioinformatics.Therefore, it is essential to develop and improve algorithms reducing their computational complexity and  increasing the precision of the solutions generated by them as well as the size of the input data.In this study two well-known algorithms for solving the problem for finding a minimum Hamiltonian cycle in weighted, undirected and complete graph (also known as Travelling Salesman Problem –- TSP) are analyzed.The first algorithm is based on the backtracking method and it always finds the optimal solution, while with the second one, the genetic algorithm (GA), finding the optimal solution is not always guaranteed.The aims of the study are to determine: (1)which of the algorithms can be used so that the resulting solution is optimal or near-optimal and the execution time be reasonable depending on the size of the input data; (2)the influence of GA parameter values on the quality of the resulting solutions for large size of the input data. The parameters determine the number of solutions in each population and the number of all generations.The analysis of the results revealed that:(1) the algorithm that finds all possible solutions can be used for graphs with a small number of vertices (not more than 20), whereas GA can be used for graphs with a large number of vertices; (2) in graphs with a small number of vertices: n<20 (and n*(n-1)/2 edges) GA always finds the optimal solution as long as  enough  solution space is set. However, the number of all Hamiltonian cycles in a complete graph with n vertices ((n-1)!/2) is bigger than the solution space; (3) all input datasets showed that with the number increase of vertices in the graph it is necessary to increase the number of the current solutions in the population. In this way GA reaches a certain rate of convergence faster, i.e.,  a generation after which the space of solutions contains only optimal solutions or near optimal ones.Acknowledgments: This work is partially supported by the project of the Bulgarian National Science Fund, entitled: “Bioinformatics research: protein folding, docking and prediction of biological activity”, NSF I02/16, 12.12.14.

scholarly journals Service Composition in IoT using Genetic algorithm and Particle swarm optimization

2020 ◽  
Vol 10 (1) ◽  
pp. 56-64 ◽  
Author(s):  
Neeti Kashyap ◽  
A. Charan Kumari ◽  
Rita Chhikara
Keyword(s):  
Genetic Algorithm ◽  
Quality Of Service ◽  
Particle Swarm Optimization ◽  
Service Composition ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Pso Algorithm ◽  
Critical Issue ◽  
Swarm Optimization

AbstractWeb service compositions are commendable in structuring innovative applications for different Internet-based business solutions. The existing services can be reused by the other applications via the web. Due to the availability of services that can serve similar functionality, suitable Service Composition (SC) is required. There is a set of candidates for each service in SC from which a suitable candidate service is picked based on certain criteria. Quality of service (QoS) is one of the criteria to select the appropriate service. A standout amongst the most important functionality presented by services in the Internet of Things (IoT) based system is the dynamic composability. In this paper, two of the metaheuristic algorithms namely Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are utilized to tackle QoS based service composition issues. QoS has turned into a critical issue in the management of web services because of the immense number of services that furnish similar functionality yet with various characteristics. Quality of service in service composition comprises of different non-functional factors, for example, service cost, execution time, availability, throughput, and reliability. Choosing appropriate SC for IoT based applications in order to optimize the QoS parameters with the fulfillment of user’s necessities has turned into a critical issue that is addressed in this paper. To obtain results via simulation, the PSO algorithm is used to solve the SC problem in IoT. This is further assessed and contrasted with GA. Experimental results demonstrate that GA can enhance the proficiency of solutions for SC problem in IoT. It can also help in identifying the optimal solution and also shows preferable outcomes over PSO.

TEST DATA GENERATION FOR SOFTWARE TESTING BASED ON QUANTUM-INSPIRED GENETIC ALGORITHM

2013 ◽  
Vol 12 (01) ◽  
pp. 1350004 ◽  
Author(s):  
CHENGYING MAO ◽  
XINXIN YU
Keyword(s):  
Genetic Algorithm ◽  
Software Testing ◽  
Test Data ◽  
Optimal Solution ◽  
Search Space ◽  
Structural Testing ◽  
Test Data Generation ◽  
Data Generation ◽  
Quantum Bit

The quality of test data has an important impact on the effect of software testing, so test data generation has always been a key task for finding the potential faults in program code. In structural testing, the primary goal is to cover some kinds of structure elements with some specific inputs. Search-based test data generation provides a rational way to handle this difficult problem. In the past, some well-known meta-heuristic search algorithms have been successfully utilized to solve this issue. In this paper, we introduce a variant of genetic algorithm (GA), called quantum-inspired genetic algorithm (QIGA), to generate the test data with stronger coverage ability. In this new algorithm, the traditional binary bit is replaced by a quantum bit (Q-bit) to enlarge the search space so as to avoid falling into local optimal solution. On the other hand, some other strategies such as quantum rotation gate and catastrophe operation are also used to improve algorithm efficiency and quality of test data. In addition, experimental analysis on eight real-world programs is performed to validate the effectiveness of our method. The results show that QIGA-based method can generate test data with higher coverage in much smaller convergence generations than GA-based method. More importantly, our proposed method is more robust for algorithm parameter change.

Modeling and Optimization for Steel Grade Selection Based on Hierarchical Genetic Algorithm

2012 ◽  
Vol 505 ◽  
pp. 203-208
Author(s):  
Jian Yi ◽  
Bin Du ◽  
Qiang Liu ◽  
Yun Lin ◽  
Ke Wei Huang
Keyword(s):  
Genetic Algorithm ◽  
Optimal Solution ◽  
Solution Space ◽  
Difficult Problem ◽  
Steel Grade ◽  
Reasonable Time ◽  
Steel Making ◽  
Steel Grades ◽  
The Mathematical Model ◽  
Hierarchical Genetic Algorithm

In steel-making process, when a furnace is charged, there are many optional steel grades for each slab. It is a difficult problem to select the appropriate steel grade for each slab. In this paper, based on the analysis of technics constraints in steel-making process, the steel grade intensivism problem is described, and the mathematical model is also established. To solve the above problem, a newly designed hierarchical genetic algorithm is proposed, where the hierarchical manner is used to decrease the solution space. The effectiveness of the approach is demonstrated by a simulation. The optimal solution can be obtained in reasonable time, which will be helpful to decrease the scraps between two steel grades while casting, to decrease the sum of surplus, and eventually to cut down the stock.

Genetic Algorithms Study in Switch Electrical Appliances Electric Arc Feature Extraction

2013 ◽  
Vol 365-366 ◽  
pp. 165-169
Author(s):  
Jing Sheng Yu ◽  
Li Li ◽  
Ting Liu
Keyword(s):  
Genetic Algorithm ◽  
Feature Extraction ◽  
Optimal Solution ◽  
Electric Arc ◽  
Network Computing ◽  
Electrical Arc ◽  
Pool Formation ◽  
Extraction Model ◽  
Crossover And Mutation

The genetic algorithm applied to switch electrical appliances electric arc feature extraction, based on genetic algorithm, the switch electrical arc feature extraction model was established. The initial pool formation, evaluation individual, reproduction, crossover and mutation have done a detailed representation. This model can eliminate the slow convergence and so easy to fall into the local minimum shortcomings of BP neural network computing graphics weights. The experiment showed that genetic algorithm can better converge to the global optimal solution, more in line with the arc Feature Extraction fact, and more effectively improving the quality of graphics extraction.

scholarly journals A Self-Adaptive Discrete PSO Algorithm with Heterogeneous Parameter Values for Dynamic TSP

2019 ◽  
Author(s):  
Łukasz Strąk ◽  
Rafał Skinderowicz ◽  
Urszula Boryczka ◽  
Arkadiusz Nowakowski
Keyword(s):  
Travelling Salesman Problem ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Discrete Particle Swarm Optimization ◽  
Travelling Salesman ◽  
Discrete Pso ◽  
Parameter Values ◽  
Positive Effect ◽  
Uniform Parameter

This paper presents a discrete particle swarm optimization (DPSO) algorithm with heterogeneous (non-uniform) parameter values for solving the dynamic travelling salesman problem (DTSP). The DTSP can be modelled as a sequence of static sub-problems, each of which is an instance of the TSP. We present a method for automatically setting the values of the DPSO parameters without three parameters, which can be defined based on the size of the problem, the size of the particle swarm, the number of iterations, and the particle neighbourhood size. We show that the diversity of parameter values has a positive effect on the quality of the generated results. We compare the performance of the proposed heterogeneous DPSO with two ant colony optimization (ACO) algorithms. The proposed algorithm outperforms the base DPSO and is competitive with the ACO.

scholarly journals Optimization of Traveling Salesman Problem Using Affinity Propagation Clustering and Genetic Algorithm

2015 ◽  
Vol 5 (4) ◽  
pp. 239-245 ◽  
Author(s):  
Ahmad Fouad El-Samak ◽  
Wesam Ashour
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Solution Space ◽  
Affinity Propagation ◽  
Computational Time ◽  
Combinatorial Optimization Problems ◽  
Affinity Propagation Clustering ◽  
Core Idea ◽  
Computation Algorithms

Abstract Combinatorial optimization problems, such as travel salesman problem, are usually NP-hard and the solution space of this problem is very large. Therefore the set of feasible solutions cannot be evaluated one by one. The simple genetic algorithm is one of the most used evolutionary computation algorithms, that give a good solution for TSP, however, it takes much computational time. In this paper, Affinity Propagation Clustering Technique (AP) is used to optimize the performance of the Genetic Algorithm (GA) for solving TSP. The core idea, which is clustering cities into smaller clusters and solving each cluster using GA separately, thus the access to the optimal solution will be in less computational time. Numerical experiments show that the proposed algorithm can give a good results for TSP problem more than the simple GA.

scholarly journals A Heuristic Algorithm for Resource Allocation/Reallocation Problem

2011 ◽  
Vol 2011 ◽  
pp. 1-11
Author(s):  
S. Raja Balachandar ◽  
K. Kannan
Keyword(s):  
Resource Allocation ◽  
Heuristic Algorithms ◽  
Optimal Solution ◽  
Heuristic Approach ◽  
Benchmark Problems ◽  
Test Problems ◽  
Large Size ◽  
Multidimensional Knapsack ◽  
Hard Problems

This paper presents a1-optheuristic approach to solve resource allocation/reallocation problem which is known as 0/1 multichoice multidimensional knapsack problem (MMKP). The intercept matrix of the constraints is employed to find optimal or near-optimal solution of the MMKP. This heuristic approach is tested for 33 benchmark problems taken from OR library of sizes upto 7000, and the results have been compared with optimum solutions. Computational complexity is proved to be of solving heuristically MMKP using this approach. The performance of our heuristic is compared with the best state-of-art heuristic algorithms with respect to the quality of the solutions found. The encouraging results especially for relatively large-size test problems indicate that this heuristic approach can successfully be used for finding good solutions for highly constrained NP-hard problems.

scholarly journals Hybrid Evolutionary-Heuristic Algorithm for Capacitor Banks Allocation

2010 ◽  
Vol 61 (6) ◽  
pp. 332-340 ◽  
Author(s):  
Marinko Barukčić ◽  
Srete Nikolovski ◽  
Franjo Jović
Keyword(s):  
Genetic Algorithm ◽  
Heuristic Algorithm ◽  
Optimal Allocation ◽  
Hybrid Methods ◽  
Optimal Solution ◽  
Distribution Networks ◽  
Computing Methods ◽  
Evolutionary Stage ◽  
Capacitor Banks

Hybrid Evolutionary-Heuristic Algorithm for Capacitor Banks Allocation The issue of optimal allocation of capacitor banks concerning power losses minimization in distribution networks are considered in this paper. This optimization problem has been recently tackled by application of contemporary soft computing methods such as: genetic algorithms, neural networks, fuzzy logic, simulated annealing, ant colony methods, and hybrid methods. An evolutionaryheuristic method has been proposed for optimal capacitor allocation in radial distribution networks. An evolutionary method based on genetic algorithm is developed. The proposed method has a reduced number of parameters compared to the usual genetic algorithm. A heuristic stage is used for improving the optimal solution given by the evolutionary stage. A new cost-voltage node index is used in the heuristic stage in order to improve the quality of solution. The efficiency of the proposed two-stage method has been tested on different test networks. The quality of solution has been verified by comparison tests with other methods on the same test networks. The proposed method has given significantly better solutions for time dependent load in the 69-bus network than found in references.

Adaptive OFDMA Resource Allocation using Modified Multi-Dimension Genetic Algorithm

2016 ◽  
Vol 12 (1) ◽  
pp. 103-113 ◽  
Author(s):  
Mohammed Ibrahim ◽  
Haider AlSabbagh
Keyword(s):  
Genetic Algorithm ◽  
Resource Allocation ◽  
Power Allocation ◽  
Communication Systems ◽  
Optimal Solution ◽  
Solution Space ◽  
Allocation Problem ◽  
Base Stations ◽  
Resource Allocation Problem ◽  
Effective Parameters

A considerable work has been conducted to cope with orthogonal frequency division multiple access (OFDMA) resource allocation with using different algorithms and methods. However, most of the available studies deal with optimizing the system for one or two parameters with simple practical condition/constraints. This paper presents analyses and simulation of dynamic OFDMA resource allocation implementation with Modified Multi-Dimension Genetic Algorithm (MDGA) which is an extension for the standard algorithm. MDGA models the resource allocation problem to find the optimal or near optimal solution for both subcarrier and power allocation for OFDMA. It takes into account the power and subcarrier constrains, channel and noise distributions, distance between user's equipment (UE) and base stations (BS), user priority weight – to approximate the most effective parameters that encounter in OFDMA systems. In the same time multi dimension genetic algorithm is used to allow exploring the solution space of resource allocation problem effectively with its different evolutionary operators: multi dimension crossover, multi dimension mutation. Four important cases are addressed and analyzed for resource allocation of OFDMA system under specific operation scenarios to meet the standard specifications for different advanced communication systems. The obtained results demonstrate that MDGA is an effective algorithm in finding the optimal or near optimal solution for both of subcarrier and power allocation of OFDMA resource allocation.

scholarly journals Limited Search Space-Based Algorithm for Dual Resource Constrained Scheduling Problem with Multilevel Product Structure

2019 ◽  
Vol 9 (19) ◽  
pp. 4005 ◽  
Author(s):  
Geunho Yang ◽  
Byung Do Chung ◽  
Sang Jin Lee
Keyword(s):  
Genetic Algorithm ◽  
Job Shop ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Solution Space ◽  
Search Space ◽  
Product Structure ◽  
Neighborhood Search ◽  
Scheduling Problem ◽  
Resource Constrained

This study addresses the dual resource constrained flexible job shop scheduling problem (DRCFJSP) with a multilevel product structure. The DRCFJSP is a strong NP-hard problem, and an efficient algorithm is essential for DRCFJSP. In this study, we propose an algorithm for the DRCFJSP with a multilevel product structure to minimize the lateness, makespan, and deviation of the workload with preemptive priorities. To efficiently solve the problem within a limited time, the search space is limited based on the possible start and end time, and focus is placed on the intensification rather than diversification, which can help the algorithm spend more time to find an optimal solution in a reasonable solution space. The performance of the proposed algorithm is compared with those of a genetic algorithm and a hybrid genetic algorithm with variable neighborhood search. The numerical experiments demonstrate that the strategy limiting the search space is effective for large and complex problems.

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